MLcomp version alpha Statistics: 2043 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 15625

Creator	jbensley
Program	svmlight-linear
Dataset	201109_obs_balanced
Task type	BinaryClassification
Created	1y250d ago

Status: Done!

Total Time	52m54s
Max Memory Usage	37M
Domain	BinaryClassification
Learn time
Train error	0.161
Predict train time
Test error	0.162
Predict test time

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..OK. (24821 examples read)
Setting default regularization parameter C=0.0000
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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WARNING: Relaxing epsilon on KT-Conditions (0.010000).
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(31963 iterations)
Optimization finished (3996 misclassified, maxdiff=0.00789).
Runtime in cpu-seconds: 2064.61
Number of SV: 15078 (including 15030 at upper bound)
L1 loss: loss=14052.93155
Norm of weight vector: |w|=0.00902
Norm of longest example vector: |x|=14154795.33556
Estimated VCdim of classifier: VCdim<=1645561879.62653
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=60.73% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>39.17% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>39.19% (rho=1.00,depth=0)
Number of kernel evaluations: 2051354
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [3158s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [3s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Reading model...OK. (15078 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..done
Runtime (without IO) in cpu-seconds: 0.01
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [6s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [3s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Reading model...OK. (15078 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..done
Runtime (without IO) in cpu-seconds: 0.00
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [4s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s]


real	52m57.618s
user	34m36.982s
sys	0m3.124s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) svmlight-linear: SVMlight for binary classification using a linear kernel (http://svmlight.joachims.org)
  • (dataset:Dataset) 201109_obs_balanced:
  • (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.162075773244336
    • numErrors: 1724
    • numExamples: 10637
    • success: true
    • time: 2
  • predict:
    • success: true
    • time: 4
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • errorRate: 0.16099270778776
    • numErrors: 3996
    • numExamples: 24821
    • success: true
    • time: 3
  • predict:
    • success: true
    • time: 6
  • strip:
    • success: true
    • time: 3
• exitCode: 0
• learn:
  • success: true
  • time: 3158
• success: true

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